Medical imaging diagnostics has been a growing area of research in the past several decades. In many cases, image fusion, or the combination of multiple associated images to form a composite image integrating the data therefrom is often desirable in a clinical setting. The first step in combining multiple associated images involves a spatial alignment of these images, a process known as image registration. Image registration is the act of spatially mapping the coordinate system of one image to the coordinate system of another image.
WO Patent application No. 02/23477 A2, assigned to Zhu, Yang-Ming and Cochoff, Steven M., and incorporated herein by reference, teaches a method of image registration by employing a registration processor to calculate a statistical measure of likelihood for two volumetric images based on an assumption that the images are probabilistically related. The likelihood is calculated using mutation probabilities (either calculated from prior knowledge of the image relationship or estimated purely from the image data) for some or all voxel pairs in the overlapping volume. The likelihood is calculated for a plurality of transformations in iterative fashion until a transformation that maximizes the likelihood is found. The transformation that maximizes likelihood provides an optimal registration of the two images.
U.S. Pat. No. 6,343,143 B1, assigned to Regis Guillemaud and Sebastien Durbec, and incorporated herein by reference, teaches a method of image registration that consists of breaking down each of the images into space components representing the distribution of the gray levels of the image, applying a phase registration method to the components to bring about a correspondence between the components of one image with those of the other image, summating all the results of the bringing into correspondence and detecting, in the image resulting from said sum, the maximum gray level defining the transformation between the two initial images.
P. Viola and W. M. Wells III teach a method (see “Alignment by maximization of mutual information,” in the proceedings of International Conference on Computer Vision, pp. 16-23, IEEE Computer Society Press, Los Alamitos, Calif., 1995,
http://citeseer.nj.nec.com/cache/papers/cs/17410/ftp:zSzzSzftp.ai.mit.eduzSzpubz SzuserszSzswzSzpaperszSziccv-95.pdf/viola95alignment.pdf) that aligns two images by adjustment of the relative pose until the mutual information between the two images is maximized.
A drawback of the above methods is that the dependence of the gray values of neighboring voxels is ignored. The assumption of independence does not hold in general. Incorporating the dependence of the gray values of neighboring voxels, i.e., the spatial information of a voxel, could improve registration.
J. P. Pluim, J. B. Maintz, and M. A. Viergever teach a method (see “Image Registration by Maximization of Combined Mutual Information and Gradient Information,” IEEE Transactions on Medical Imaging, vol. 19, no. 8, pp. 809-814, 2000, http://www.isi.uu.nl/People/Josien/Papers/Pluim_TMI—19—8.pdf) that incorporates gradient information of the involved images into the registration process using the mutual information technique. This method requires separate gradient images (information) in addition to intensity images. It would be desirable to include spatial information of a voxel within the mutual information technology without the need for separate gradient images.
There is a need therefore for an improved image registration method that overcomes the problems set forth above.
These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings.